Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces

  • Pablo Samuel Castro
  • Daqing Zhang
  • Shijian Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7319)


Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city’s road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city’s air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance.


Road Network Road Segment Congestion Level Large Scale Database Markov Logic Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pablo Samuel Castro
    • 1
  • Daqing Zhang
    • 1
  • Shijian Li
    • 2
  1. 1.Institut Telecom SudParisEvry CedexFrance
  2. 2.Zhejiang UniversityHangzhouP.R. China

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